As smart cities emerge worldwide, the integration of real-time data in
multiple sectors, including transportation, energy, and public services, is starting to
surface. Among the unexplored frontiers within this data landscape, vital for fortifying
resilient and efficient urban services, lies the realm of natural disaster prediction and
mitigation. This chapter aims to address the first phase in developing intelligent, datadriven tools for natural disaster prediction in smart city infrastructure. The integration
of Artificial Intelligence (AI) and Machine Learning (ML) models within smart city
infrastructure stands as a pivotal advancement, offering an array of benefits in disaster
prediction and management. Techniques such as neural networks, decision trees,
random forests, and support vector machines have proven instrumental in this regard.
This chapter is of high relevance to the field currently due to the increasing
sophistication and range of AI technologies, as well as the increasing capacity for realtime data collection facilitated by smart city technology. The focus on combining AI
approaches with the unique challenges and opportunities presented by smart city
infrastructure makes this a timely and essential project for the field and an excellent
driver for knowledge exchange with both the academic community and industry in both
AI and smart city development. Despite facing obstacles, such as legal and social
concerns, the ongoing progress and widespread acceptance of AI-based solutions offer
significant potential for strengthening disaster preparedness and improving the overall
quality of life in cities.
Keywords: Artificial intelligence, hadoop distributed file system, AI-based optimization, real-time monitoring, unmanned aerial vehicles (UAVs).
